Overview

Dataset statistics

Number of variables26
Number of observations2056767
Missing cells161
Missing cells (%)< 0.1%
Duplicate rows88
Duplicate rows (%)< 0.1%
Total size in memory423.7 MiB
Average record size in memory216.0 B

Variable types

Categorical8
DateTime2
Numeric16

Alerts

Dataset has 88 (< 0.1%) duplicate rowsDuplicates
VIN has a high cardinality: 1872792 distinct valuesHigh cardinality
MotorType has a high cardinality: 35862 distinct valuesHigh cardinality
Make has a high cardinality: 844 distinct valuesHigh cardinality
Model has a high cardinality: 14817 distinct valuesHigh cardinality
Type is highly imbalanced (76.8%)Imbalance
Make is highly imbalanced (56.2%)Imbalance
VehicleType is highly imbalanced (62.9%)Imbalance
VehicleClass is highly imbalanced (78.5%)Imbalance
Result is highly imbalanced (63.8%)Imbalance
Defects9 is highly skewed (γ1 = 35.7409099)Skewed
VIN is uniformly distributedUniform
DefectsA has 178718 (8.7%) zerosZeros
DefectsB has 1818491 (88.4%) zerosZeros
DefectsC has 2019204 (98.2%) zerosZeros
Defects0 has 1854322 (90.2%) zerosZeros
Defects1 has 1029946 (50.1%) zerosZeros
Defects2 has 1751198 (85.1%) zerosZeros
Defects3 has 1777189 (86.4%) zerosZeros
Defects4 has 1197647 (58.2%) zerosZeros
Defects5 has 681951 (33.2%) zerosZeros
Defects6 has 558268 (27.1%) zerosZeros
Defects7 has 2010529 (97.8%) zerosZeros
Defects8 has 2015787 (98.0%) zerosZeros
Defects9 has 2053169 (99.8%) zerosZeros

Reproduction

Analysis started2023-04-17 14:08:05.405459
Analysis finished2023-04-17 14:09:39.024564
Duration1 minute and 33.62 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Type
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.4 MiB
pravidelná
1745774 
opakovaná
 
155903
před registrací
 
105768
evidenční
 
37898
na žádost zákazníka
 
6780
Other values (8)
 
4644

Length

Max length37
Median length10
Mean length10.228699
Min length3

Characters and Unicode

Total characters21038050
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpravidelná
2nd rowpravidelná
3rd rowevidenční
4th rowpravidelná
5th rowopakovaná

Common Values

ValueCountFrequency (%)
pravidelná 1745774
84.9%
opakovaná 155903
 
7.6%
před registrací 105768
 
5.1%
evidenční 37898
 
1.8%
na žádost zákazníka 6780
 
0.3%
před registrací - opakovaná 2882
 
0.1%
před schválením tech. zp. 1015
 
< 0.1%
silniční - opakovaná po DN 438
 
< 0.1%
silniční - opakovaná 186
 
< 0.1%
před schválením tech. zp. - opakovaná 56
 
< 0.1%
Other values (3) 67
 
< 0.1%

Length

2023-04-17T16:09:39.065456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pravidelná 1745774
79.7%
opakovaná 159468
 
7.3%
před 109721
 
5.0%
registrací 108650
 
5.0%
evidenční 37898
 
1.7%
na 6780
 
0.3%
žádost 6780
 
0.3%
zákazníka 6780
 
0.3%
3565
 
0.2%
schválením 1071
 
< 0.1%
Other values (7) 3709
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 2193731
10.4%
e 2042114
9.7%
p 2016472
9.6%
n 1996979
9.5%
r 1963074
9.3%
v 1944211
9.2%
á 1919904
9.1%
d 1900173
9.0%
i 1893570
9.0%
l 1747469
8.3%
Other values (20) 1420353
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20897930
99.3%
Space Separator 133429
 
0.6%
Dash Punctuation 3565
 
< 0.1%
Other Punctuation 2142
 
< 0.1%
Uppercase Letter 984
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2193731
10.5%
e 2042114
9.8%
p 2016472
9.6%
n 1996979
9.6%
r 1963074
9.4%
v 1944211
9.3%
á 1919904
9.2%
d 1900173
9.1%
i 1893570
9.1%
l 1747469
8.4%
Other values (13) 1280233
6.1%
Uppercase Letter
ValueCountFrequency (%)
D 474
48.2%
N 438
44.5%
A 36
 
3.7%
R 36
 
3.7%
Space Separator
ValueCountFrequency (%)
133429
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3565
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20898914
99.3%
Common 139136
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2193731
10.5%
e 2042114
9.8%
p 2016472
9.6%
n 1996979
9.6%
r 1963074
9.4%
v 1944211
9.3%
á 1919904
9.2%
d 1900173
9.1%
i 1893570
9.1%
l 1747469
8.4%
Other values (17) 1281217
6.1%
Common
ValueCountFrequency (%)
133429
95.9%
- 3565
 
2.6%
. 2142
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18808038
89.4%
None 2230012
 
10.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2193731
11.7%
e 2042114
10.9%
p 2016472
10.7%
n 1996979
10.6%
r 1963074
10.4%
v 1944211
10.3%
d 1900173
10.1%
i 1893570
10.1%
l 1747469
9.3%
o 326154
 
1.7%
Other values (15) 784091
 
4.2%
None
ValueCountFrequency (%)
á 1919904
86.1%
í 155054
 
7.0%
ř 109752
 
4.9%
č 38522
 
1.7%
ž 6780
 
0.3%

VIN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1872792
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Memory size31.4 MiB
XLRAE55CF0L234415
 
7
WMAH18ZZX7W075623
 
6
VSSZZZ1LZXR004990
 
6
TNAA2N0006A004438
 
6
WBAAX71090JW82424
 
6
Other values (1872787)
2056736 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters34965039
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1697882 ?
Unique (%)82.6%

Sample

1st rowTAH31100EF1112050
2nd rowTN9FV1Z00RAAM5032
3rd rowRLVMEM5057VN00070
4th rowZAR162B4000075261
5th rowZAR93000002261774

Common Values

ValueCountFrequency (%)
XLRAE55CF0L234415 7
 
< 0.1%
WMAH18ZZX7W075623 6
 
< 0.1%
VSSZZZ1LZXR004990 6
 
< 0.1%
TNAA2N0006A004438 6
 
< 0.1%
WBAAX71090JW82424 6
 
< 0.1%
ZFA24400007342405 6
 
< 0.1%
VF644AGL000007672 5
 
< 0.1%
TMBEA45J3B3129573 5
 
< 0.1%
Y3M55163350000033 5
 
< 0.1%
WF0VXXBDFV5D83825 5
 
< 0.1%
Other values (1872782) 2056710
> 99.9%

Length

2023-04-17T16:09:39.184549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xlrae55cf0l234415 7
 
< 0.1%
vsszzz1lzxr004990 6
 
< 0.1%
tnaa2n0006a004438 6
 
< 0.1%
wbaax71090jw82424 6
 
< 0.1%
zfa24400007342405 6
 
< 0.1%
wmah18zzx7w075623 6
 
< 0.1%
zcfc3590005424141 5
 
< 0.1%
seyzmyfzhdn095608 5
 
< 0.1%
vf3ydpmau12831147 5
 
< 0.1%
w0l0ahm758g194955 5
 
< 0.1%
Other values (1872782) 2056710
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 3260592
 
9.3%
1 2776588
 
7.9%
2 2260881
 
6.5%
3 2125389
 
6.1%
6 1860419
 
5.3%
4 1836863
 
5.3%
5 1810639
 
5.2%
7 1633722
 
4.7%
Z 1557133
 
4.5%
8 1536475
 
4.4%
Other values (29) 14306338
40.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20478623
58.6%
Uppercase Letter 14486380
41.4%
Dash Punctuation 27
 
< 0.1%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 1557133
 
10.7%
B 1226376
 
8.5%
F 1178102
 
8.1%
W 1065050
 
7.4%
M 930325
 
6.4%
T 862663
 
6.0%
V 786618
 
5.4%
A 760363
 
5.2%
X 647430
 
4.5%
C 542105
 
3.7%
Other values (16) 4930215
34.0%
Decimal Number
ValueCountFrequency (%)
0 3260592
15.9%
1 2776588
13.6%
2 2260881
11.0%
3 2125389
10.4%
6 1860419
9.1%
4 1836863
9.0%
5 1810639
8.8%
7 1633722
8.0%
8 1536475
7.5%
9 1377055
6.7%
Other Punctuation
ValueCountFrequency (%)
/ 5
55.6%
. 4
44.4%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20478659
58.6%
Latin 14486380
41.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 1557133
 
10.7%
B 1226376
 
8.5%
F 1178102
 
8.1%
W 1065050
 
7.4%
M 930325
 
6.4%
T 862663
 
6.0%
V 786618
 
5.4%
A 760363
 
5.2%
X 647430
 
4.5%
C 542105
 
3.7%
Other values (16) 4930215
34.0%
Common
ValueCountFrequency (%)
0 3260592
15.9%
1 2776588
13.6%
2 2260881
11.0%
3 2125389
10.4%
6 1860419
9.1%
4 1836863
9.0%
5 1810639
8.8%
7 1633722
8.0%
8 1536475
7.5%
9 1377055
6.7%
Other values (3) 36
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34965039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3260592
 
9.3%
1 2776588
 
7.9%
2 2260881
 
6.5%
3 2125389
 
6.1%
6 1860419
 
5.3%
4 1836863
 
5.3%
5 1810639
 
5.2%
7 1633722
 
4.7%
Z 1557133
 
4.5%
8 1536475
 
4.4%
Other values (29) 14306338
40.9%

Date
Date

Distinct459573
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size31.4 MiB
Minimum2019-01-02 06:09:03.123000
Maximum2019-12-31 00:00:00
2023-04-17T16:09:39.261660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:39.343347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MotorType
Categorical

Distinct35862
Distinct (%)1.7%
Missing2
Missing (%)< 0.1%
Memory size31.4 MiB
ALH
 
29062
781.136M
 
27736
BXE
 
23373
ASV
 
20510
AGR
 
20406
Other values (35857)
1935678 

Length

Max length17
Median length16
Mean length4.5979925
Min length1

Characters and Unicode

Total characters9456990
Distinct characters89
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18019 ?
Unique (%)0.9%

Sample

1st row60D/3
2nd row781.135N
3rd rowFBGM
4th rowARO6166
5th row67601

Common Values

ValueCountFrequency (%)
ALH 29062
 
1.4%
781.136M 27736
 
1.3%
BXE 23373
 
1.1%
ASV 20510
 
1.0%
AGR 20406
 
1.0%
BME 18807
 
0.9%
AZQ 18368
 
0.9%
AQW 18209
 
0.9%
781.136 M 17763
 
0.9%
AWY 17642
 
0.9%
Other values (35852) 1844889
89.7%

Length

2023-04-17T16:09:39.431568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alh 29103
 
1.3%
781.136m 28020
 
1.2%
m 24349
 
1.0%
781.136 24240
 
1.0%
bxe 23374
 
1.0%
7 22692
 
1.0%
asv 20520
 
0.9%
agr 20482
 
0.9%
bme 18808
 
0.8%
azq 18375
 
0.8%
Other values (26875) 2094752
90.1%

Most occurring characters

ValueCountFrequency (%)
A 836080
 
8.8%
1 614605
 
6.5%
B 531984
 
5.6%
F 477216
 
5.0%
4 410681
 
4.3%
0 402129
 
4.3%
D 395475
 
4.2%
6 318934
 
3.4%
E 302312
 
3.2%
7 286930
 
3.0%
Other values (79) 4880644
51.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5716685
60.4%
Decimal Number 3213328
34.0%
Space Separator 268873
 
2.8%
Other Punctuation 202902
 
2.1%
Dash Punctuation 51980
 
0.5%
Math Symbol 1672
 
< 0.1%
Open Punctuation 624
 
< 0.1%
Close Punctuation 614
 
< 0.1%
Lowercase Letter 311
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 836080
14.6%
B 531984
 
9.3%
F 477216
 
8.3%
D 395475
 
6.9%
E 302312
 
5.3%
M 278017
 
4.9%
C 276130
 
4.8%
H 270427
 
4.7%
K 222507
 
3.9%
X 213110
 
3.7%
Other values (24) 1913427
33.5%
Lowercase Letter
ValueCountFrequency (%)
a 36
 
11.6%
b 30
 
9.6%
c 28
 
9.0%
s 21
 
6.8%
d 20
 
6.4%
z 18
 
5.8%
f 15
 
4.8%
i 15
 
4.8%
k 13
 
4.2%
e 13
 
4.2%
Other values (17) 102
32.8%
Decimal Number
ValueCountFrequency (%)
1 614605
19.1%
4 410681
12.8%
0 402129
12.5%
6 318934
9.9%
7 286930
8.9%
8 285675
8.9%
2 281517
8.8%
3 266590
8.3%
9 210959
 
6.6%
5 135308
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 181087
89.2%
/ 16062
 
7.9%
, 3634
 
1.8%
* 2075
 
1.0%
? 40
 
< 0.1%
; 3
 
< 0.1%
\ 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 622
99.7%
[ 1
 
0.2%
{ 1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 611
99.5%
] 2
 
0.3%
} 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
+ 1671
99.9%
= 1
 
0.1%
Space Separator
ValueCountFrequency (%)
268873
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51980
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5716996
60.5%
Common 3739994
39.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 836080
14.6%
B 531984
 
9.3%
F 477216
 
8.3%
D 395475
 
6.9%
E 302312
 
5.3%
M 278017
 
4.9%
C 276130
 
4.8%
H 270427
 
4.7%
K 222507
 
3.9%
X 213110
 
3.7%
Other values (51) 1913738
33.5%
Common
ValueCountFrequency (%)
1 614605
16.4%
4 410681
11.0%
0 402129
10.8%
6 318934
8.5%
7 286930
7.7%
8 285675
7.6%
2 281517
7.5%
268873
7.2%
3 266590
7.1%
9 210959
 
5.6%
Other values (18) 393101
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9455555
> 99.9%
None 1435
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 836080
 
8.8%
1 614605
 
6.5%
B 531984
 
5.6%
F 477216
 
5.0%
4 410681
 
4.3%
0 402129
 
4.3%
D 395475
 
4.2%
6 318934
 
3.4%
E 302312
 
3.2%
7 286930
 
3.0%
Other values (69) 4879209
51.6%
None
ValueCountFrequency (%)
Š 1343
93.6%
Č 19
 
1.3%
Á 18
 
1.3%
Ý 15
 
1.0%
Í 14
 
1.0%
Ž 8
 
0.6%
Ř 8
 
0.6%
š 5
 
0.3%
Ě 4
 
0.3%
´ 1
 
0.1%

Make
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct844
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.4 MiB
ŠKODA
580328 
FORD
179233 
RENAULT
126097 
VW
125769 
PEUGEOT
124820 
Other values (839)
920520 

Length

Max length28
Median length26
Mean length5.5293458
Min length2

Characters and Unicode

Total characters11372576
Distinct characters79
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique279 ?
Unique (%)< 0.1%

Sample

1st rowAEBI
2nd rowAGM
3rd rowAIE
4th rowALFA ROMEO
5th rowALFA ROMEO

Common Values

ValueCountFrequency (%)
ŠKODA 580328
28.2%
FORD 179233
 
8.7%
RENAULT 126097
 
6.1%
VW 125769
 
6.1%
PEUGEOT 124820
 
6.1%
VOLKSWAGEN 92143
 
4.5%
CITROËN 90213
 
4.4%
OPEL 77027
 
3.7%
FIAT 62922
 
3.1%
MERCEDES-BENZ 57522
 
2.8%
Other values (834) 540693
26.3%

Length

2023-04-17T16:09:39.517451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
škoda 580334
28.0%
ford 179238
 
8.6%
renault 126135
 
6.1%
vw 125769
 
6.1%
peugeot 124825
 
6.0%
volkswagen 92143
 
4.4%
citroën 90213
 
4.4%
opel 77027
 
3.7%
fiat 62925
 
3.0%
mercedes-benz 57523
 
2.8%
Other values (846) 556966
26.9%

Most occurring characters

ValueCountFrequency (%)
O 1382135
12.2%
A 1317621
 
11.6%
D 988973
 
8.7%
E 908208
 
8.0%
K 729285
 
6.4%
Š 580346
 
5.1%
T 565272
 
5.0%
R 533106
 
4.7%
N 525809
 
4.6%
I 439048
 
3.9%
Other values (69) 3402773
29.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11297350
99.3%
Dash Punctuation 58282
 
0.5%
Space Separator 16342
 
0.1%
Lowercase Letter 326
 
< 0.1%
Other Punctuation 202
 
< 0.1%
Decimal Number 68
 
< 0.1%
Math Symbol 4
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1382135
12.2%
A 1317621
 
11.7%
D 988973
 
8.8%
E 908208
 
8.0%
K 729285
 
6.5%
Š 580346
 
5.1%
T 565272
 
5.0%
R 533106
 
4.7%
N 525809
 
4.7%
I 439048
 
3.9%
Other values (28) 3327547
29.5%
Lowercase Letter
ValueCountFrequency (%)
a 74
22.7%
l 52
16.0%
u 27
 
8.3%
x 25
 
7.7%
h 24
 
7.4%
o 23
 
7.1%
e 16
 
4.9%
i 13
 
4.0%
d 11
 
3.4%
n 11
 
3.4%
Other values (14) 50
15.3%
Decimal Number
ValueCountFrequency (%)
0 19
27.9%
5 14
20.6%
1 11
16.2%
3 8
11.8%
2 4
 
5.9%
7 4
 
5.9%
6 4
 
5.9%
8 3
 
4.4%
9 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 172
85.1%
/ 29
 
14.4%
& 1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 58282
100.0%
Space Separator
ValueCountFrequency (%)
16342
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11297676
99.3%
Common 74900
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1382135
12.2%
A 1317621
 
11.7%
D 988973
 
8.8%
E 908208
 
8.0%
K 729285
 
6.5%
Š 580346
 
5.1%
T 565272
 
5.0%
R 533106
 
4.7%
N 525809
 
4.7%
I 439048
 
3.9%
Other values (52) 3327873
29.5%
Common
ValueCountFrequency (%)
- 58282
77.8%
16342
 
21.8%
. 172
 
0.2%
/ 29
 
< 0.1%
0 19
 
< 0.1%
5 14
 
< 0.1%
1 11
 
< 0.1%
3 8
 
< 0.1%
+ 4
 
< 0.1%
2 4
 
< 0.1%
Other values (7) 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10701857
94.1%
None 670719
 
5.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1382135
12.9%
A 1317621
12.3%
D 988973
 
9.2%
E 908208
 
8.5%
K 729285
 
6.8%
T 565272
 
5.3%
R 533106
 
5.0%
N 525809
 
4.9%
I 439048
 
4.1%
U 422867
 
4.0%
Other values (55) 2889533
27.0%
None
ValueCountFrequency (%)
Š 580346
86.5%
Ë 90213
 
13.5%
Č 43
 
< 0.1%
Á 42
 
< 0.1%
Ü 41
 
< 0.1%
Ö 21
 
< 0.1%
š 4
 
< 0.1%
Ž 2
 
< 0.1%
É 2
 
< 0.1%
Ě 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

VehicleType
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.4 MiB
OSOBNÍ AUTOMOBIL
1707753 
NÁKLADNÍ AUTOMOBIL
319963 
AUTOBUS
 
15795
MOTOCYKL
 
13256

Length

Max length18
Median length16
Mean length16.190456
Min length7

Characters and Unicode

Total characters33299995
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNÁKLADNÍ AUTOMOBIL
2nd rowNÁKLADNÍ AUTOMOBIL
3rd rowMOTOCYKL
4th rowOSOBNÍ AUTOMOBIL
5th rowOSOBNÍ AUTOMOBIL

Common Values

ValueCountFrequency (%)
OSOBNÍ AUTOMOBIL 1707753
83.0%
NÁKLADNÍ AUTOMOBIL 319963
 
15.6%
AUTOBUS 15795
 
0.8%
MOTOCYKL 13256
 
0.6%

Length

2023-04-17T16:09:39.592790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T16:09:39.682066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
automobil 2027716
49.6%
osobní 1707753
41.8%
nákladní 319963
 
7.8%
autobus 15795
 
0.4%
motocykl 13256
 
0.3%

Most occurring characters

ValueCountFrequency (%)
O 7513245
22.6%
B 3751264
11.3%
A 2363474
 
7.1%
L 2360935
 
7.1%
N 2347679
 
7.1%
U 2059306
 
6.2%
T 2056767
 
6.2%
M 2040972
 
6.1%
2027716
 
6.1%
I 2027716
 
6.1%
Other values (7) 4750921
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 31272279
93.9%
Space Separator 2027716
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 7513245
24.0%
B 3751264
12.0%
A 2363474
 
7.6%
L 2360935
 
7.5%
N 2347679
 
7.5%
U 2059306
 
6.6%
T 2056767
 
6.6%
M 2040972
 
6.5%
I 2027716
 
6.5%
Í 2027716
 
6.5%
Other values (6) 2723205
 
8.7%
Space Separator
ValueCountFrequency (%)
2027716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31272279
93.9%
Common 2027716
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 7513245
24.0%
B 3751264
12.0%
A 2363474
 
7.6%
L 2360935
 
7.5%
N 2347679
 
7.5%
U 2059306
 
6.6%
T 2056767
 
6.6%
M 2040972
 
6.5%
I 2027716
 
6.5%
Í 2027716
 
6.5%
Other values (6) 2723205
 
8.7%
Common
ValueCountFrequency (%)
2027716
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30952316
92.9%
None 2347679
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 7513245
24.3%
B 3751264
12.1%
A 2363474
 
7.6%
L 2360935
 
7.6%
N 2347679
 
7.6%
U 2059306
 
6.7%
T 2056767
 
6.6%
M 2040972
 
6.6%
2027716
 
6.6%
I 2027716
 
6.6%
Other values (5) 2403242
 
7.8%
None
ValueCountFrequency (%)
Í 2027716
86.4%
Á 319963
 
13.6%

Model
Categorical

Distinct14817
Distinct (%)0.7%
Missing159
Missing (%)< 0.1%
Memory size31.4 MiB
OCTAVIA
196043 
FABIA
159011 
FELICIA
 
67415
FOCUS
 
49920
GOLF
 
45330
Other values (14812)
1538889 

Length

Max length30
Median length27
Mean length6.2487183
Min length1

Characters and Unicode

Total characters12851164
Distinct characters75
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7051 ?
Unique (%)0.3%

Sample

1st rowMT 740
2nd rowVARIANT
3rd rowMAGMAX 275 HT
4th row75
5th row145

Common Values

ValueCountFrequency (%)
OCTAVIA 196043
 
9.5%
FABIA 159011
 
7.7%
FELICIA 67415
 
3.3%
FOCUS 49920
 
2.4%
GOLF 45330
 
2.2%
FABIA COMBI 45224
 
2.2%
OCTAVIA COMBI 32042
 
1.6%
TRANSIT 29978
 
1.5%
206 29610
 
1.4%
MONDEO 27506
 
1.3%
Other values (14807) 1374529
66.8%

Length

2023-04-17T16:09:39.761666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
octavia 228094
 
9.4%
fabia 207232
 
8.5%
combi 92101
 
3.8%
felicia 81355
 
3.3%
golf 55629
 
2.3%
focus 55622
 
2.3%
passat 48232
 
2.0%
megane 45782
 
1.9%
transit 32348
 
1.3%
206 30115
 
1.2%
Other values (9528) 1553933
63.9%

Most occurring characters

ValueCountFrequency (%)
A 2035363
15.8%
I 1120825
 
8.7%
O 983785
 
7.7%
C 827558
 
6.4%
T 796981
 
6.2%
R 671631
 
5.2%
E 643394
 
5.0%
S 584430
 
4.5%
F 512177
 
4.0%
N 467450
 
3.6%
Other values (65) 4207570
32.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11397915
88.7%
Decimal Number 1059463
 
8.2%
Space Separator 373843
 
2.9%
Lowercase Letter 19943
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2035363
17.9%
I 1120825
9.8%
O 983785
 
8.6%
C 827558
 
7.3%
T 796981
 
7.0%
R 671631
 
5.9%
E 643394
 
5.6%
S 584430
 
5.1%
F 512177
 
4.5%
N 467450
 
4.1%
Other values (25) 2754321
24.2%
Lowercase Letter
ValueCountFrequency (%)
i 12303
61.7%
o 1729
 
8.7%
r 1237
 
6.2%
a 1232
 
6.2%
e 593
 
3.0%
x 515
 
2.6%
n 500
 
2.5%
d 376
 
1.9%
t 347
 
1.7%
s 216
 
1.1%
Other values (19) 895
 
4.5%
Decimal Number
ValueCountFrequency (%)
0 263854
24.9%
3 135627
12.8%
2 132348
12.5%
1 111195
10.5%
6 101960
 
9.6%
5 94182
 
8.9%
4 87594
 
8.3%
7 64686
 
6.1%
8 47619
 
4.5%
9 20398
 
1.9%
Space Separator
ValueCountFrequency (%)
373843
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11417858
88.8%
Common 1433306
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2035363
17.8%
I 1120825
9.8%
O 983785
 
8.6%
C 827558
 
7.2%
T 796981
 
7.0%
R 671631
 
5.9%
E 643394
 
5.6%
S 584430
 
5.1%
F 512177
 
4.5%
N 467450
 
4.1%
Other values (54) 2774264
24.3%
Common
ValueCountFrequency (%)
373843
26.1%
0 263854
18.4%
3 135627
 
9.5%
2 132348
 
9.2%
1 111195
 
7.8%
6 101960
 
7.1%
5 94182
 
6.6%
4 87594
 
6.1%
7 64686
 
4.5%
8 47619
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12850014
> 99.9%
None 1150
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2035363
15.8%
I 1120825
 
8.7%
O 983785
 
7.7%
C 827558
 
6.4%
T 796981
 
6.2%
R 671631
 
5.2%
E 643394
 
5.0%
S 584430
 
4.5%
F 512177
 
4.0%
N 467450
 
3.6%
Other values (53) 4206420
32.7%
None
ValueCountFrequency (%)
Á 705
61.3%
É 384
33.4%
á 18
 
1.6%
Ý 10
 
0.9%
Č 10
 
0.9%
Ó 9
 
0.8%
Š 4
 
0.3%
Í 3
 
0.3%
Ö 3
 
0.3%
ü 2
 
0.2%
Other values (2) 2
 
0.2%

VehicleClass
Categorical

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.4 MiB
M1
1668016 
N1
200922 
N3
 
51724
N2
 
40166
M1G
 
39737
Other values (35)
 
56202

Length

Max length7
Median length2
Mean length2.0351775
Min length1

Characters and Unicode

Total characters4185886
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowN2G
2nd rowN1
3rd rowLE
4th rowM1
5th rowM1

Common Values

ValueCountFrequency (%)
M1 1668016
81.1%
N1 200922
 
9.8%
N3 51724
 
2.5%
N2 40166
 
2.0%
M1G 39737
 
1.9%
N1G 15910
 
0.8%
M3 14450
 
0.7%
N3G 10585
 
0.5%
LC 5795
 
0.3%
L3e 3715
 
0.2%
Other values (30) 5747
 
0.3%

Length

2023-04-17T16:09:39.838360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1 1668016
81.1%
n1 200922
 
9.8%
n3 51724
 
2.5%
n2 40166
 
2.0%
m1g 39737
 
1.9%
n1g 15910
 
0.8%
m3 14450
 
0.7%
n3g 10585
 
0.5%
lc 5795
 
0.3%
l3e 3715
 
0.2%
Other values (30) 5747
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 1925304
46.0%
M 1723548
41.2%
N 319963
 
7.6%
3 80684
 
1.9%
G 66889
 
1.6%
2 42219
 
1.0%
L 13255
 
0.3%
C 5795
 
0.1%
e 4956
 
0.1%
A 1824
 
< 0.1%
Other values (11) 1449
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2132098
50.9%
Decimal Number 2048661
48.9%
Lowercase Letter 4956
 
0.1%
Dash Punctuation 171
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1723548
80.8%
N 319963
 
15.0%
G 66889
 
3.1%
L 13255
 
0.6%
C 5795
 
0.3%
A 1824
 
0.1%
E 680
 
< 0.1%
B 138
 
< 0.1%
D 2
 
< 0.1%
T 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1925304
94.0%
3 80684
 
3.9%
2 42219
 
2.1%
7 307
 
< 0.1%
6 88
 
< 0.1%
5 55
 
< 0.1%
4 4
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 4956
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2137054
51.1%
Common 2048832
48.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1723548
80.7%
N 319963
 
15.0%
G 66889
 
3.1%
L 13255
 
0.6%
C 5795
 
0.3%
e 4956
 
0.2%
A 1824
 
0.1%
E 680
 
< 0.1%
B 138
 
< 0.1%
D 2
 
< 0.1%
Other values (3) 4
 
< 0.1%
Common
ValueCountFrequency (%)
1 1925304
94.0%
3 80684
 
3.9%
2 42219
 
2.1%
7 307
 
< 0.1%
- 171
 
< 0.1%
6 88
 
< 0.1%
5 55
 
< 0.1%
4 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4185886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1925304
46.0%
M 1723548
41.2%
N 319963
 
7.6%
3 80684
 
1.9%
G 66889
 
1.6%
2 42219
 
1.0%
L 13255
 
0.3%
C 5795
 
0.1%
e 4956
 
0.1%
A 1824
 
< 0.1%
Other values (11) 1449
 
< 0.1%
Distinct12003
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size31.4 MiB
Minimum1900-01-21 00:00:00
Maximum2019-12-20 00:00:00
2023-04-17T16:09:39.917088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:40.004715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Km
Real number (ℝ)

Distinct455741
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207148.7
Minimum0
Maximum999999
Zeros2899
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:40.100277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57185
Q1131794
median191533
Q3257095
95-th percentile401071.7
Maximum999999
Range999999
Interquartile range (IQR)125301

Descriptive statistics

Standard deviation119483.59
Coefficient of variation (CV)0.57680105
Kurtosis7.332143
Mean207148.7
Median Absolute Deviation (MAD)62396
Skewness1.9393245
Sum4.2605661 × 1011
Variance1.4276328 × 1010
MonotonicityNot monotonic
2023-04-17T16:09:40.196527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2899
 
0.1%
399999 104
 
< 0.1%
3 68
 
< 0.1%
4 67
 
< 0.1%
999999 54
 
< 0.1%
5 42
 
< 0.1%
10 39
 
< 0.1%
11 38
 
< 0.1%
248601 35
 
< 0.1%
7 33
 
< 0.1%
Other values (455731) 2053388
99.8%
ValueCountFrequency (%)
0 2899
0.1%
1 32
 
< 0.1%
2 22
 
< 0.1%
3 68
 
< 0.1%
4 67
 
< 0.1%
5 42
 
< 0.1%
6 28
 
< 0.1%
7 33
 
< 0.1%
8 30
 
< 0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
999999 54
< 0.1%
999996 1
 
< 0.1%
999958 1
 
< 0.1%
999932 1
 
< 0.1%
999922 1
 
< 0.1%
999862 1
 
< 0.1%
999853 1
 
< 0.1%
999797 1
 
< 0.1%
999779 1
 
< 0.1%
999742 1
 
< 0.1%

Result
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.4 MiB
způsobilé
1811883 
částečně způsobilé
225680 
nezpůsobilé
 
19204

Length

Max length18
Median length9
Mean length10.006204
Min length9

Characters and Unicode

Total characters20580431
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowzpůsobilé
2nd rowzpůsobilé
3rd rowčástečně způsobilé
4th rowzpůsobilé
5th rowzpůsobilé

Common Values

ValueCountFrequency (%)
způsobilé 1811883
88.1%
částečně způsobilé 225680
 
11.0%
nezpůsobilé 19204
 
0.9%

Length

2023-04-17T16:09:40.273300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T16:09:40.358652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
způsobilé 2037563
89.3%
částečně 225680
 
9.9%
nezpůsobilé 19204
 
0.8%

Most occurring characters

ValueCountFrequency (%)
s 2282447
11.1%
z 2056767
10.0%
p 2056767
10.0%
ů 2056767
10.0%
o 2056767
10.0%
b 2056767
10.0%
i 2056767
10.0%
l 2056767
10.0%
é 2056767
10.0%
č 451360
 
2.2%
Other values (6) 1392488
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20354751
98.9%
Space Separator 225680
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 2282447
11.2%
z 2056767
10.1%
p 2056767
10.1%
ů 2056767
10.1%
o 2056767
10.1%
b 2056767
10.1%
i 2056767
10.1%
l 2056767
10.1%
é 2056767
10.1%
č 451360
 
2.2%
Other values (5) 1166808
5.7%
Space Separator
ValueCountFrequency (%)
225680
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20354751
98.9%
Common 225680
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 2282447
11.2%
z 2056767
10.1%
p 2056767
10.1%
ů 2056767
10.1%
o 2056767
10.1%
b 2056767
10.1%
i 2056767
10.1%
l 2056767
10.1%
é 2056767
10.1%
č 451360
 
2.2%
Other values (5) 1166808
5.7%
Common
ValueCountFrequency (%)
225680
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15564177
75.6%
None 5016254
 
24.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 2282447
14.7%
z 2056767
13.2%
p 2056767
13.2%
o 2056767
13.2%
b 2056767
13.2%
i 2056767
13.2%
l 2056767
13.2%
e 244884
 
1.6%
n 244884
 
1.6%
t 225680
 
1.4%
None
ValueCountFrequency (%)
ů 2056767
41.0%
é 2056767
41.0%
č 451360
 
9.0%
á 225680
 
4.5%
ě 225680
 
4.5%

Weekday
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9464621
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:40.415062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3866678
Coefficient of variation (CV)0.4706213
Kurtosis-1.1122952
Mean2.9464621
Median Absolute Deviation (MAD)1
Skewness0.10038876
Sum6060186
Variance1.9228477
MonotonicityNot monotonic
2023-04-17T16:09:40.467544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 444205
21.6%
4 434641
21.1%
2 434609
21.1%
1 405098
19.7%
5 314663
15.3%
6 23481
 
1.1%
7 70
 
< 0.1%
ValueCountFrequency (%)
1 405098
19.7%
2 434609
21.1%
3 444205
21.6%
4 434641
21.1%
5 314663
15.3%
6 23481
 
1.1%
7 70
 
< 0.1%
ValueCountFrequency (%)
7 70
 
< 0.1%
6 23481
 
1.1%
5 314663
15.3%
4 434641
21.1%
3 444205
21.6%
2 434609
21.1%
1 405098
19.7%

DefectsA
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9954258
Minimum0
Maximum39
Zeros178718
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:40.540405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile7
Maximum39
Range39
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2511218
Coefficient of variation (CV)0.75151978
Kurtosis2.3813091
Mean2.9954258
Median Absolute Deviation (MAD)2
Skewness1.1601938
Sum6160893
Variance5.0675492
MonotonicityNot monotonic
2023-04-17T16:09:40.621075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 436018
21.2%
2 386054
18.8%
3 332961
16.2%
4 272524
13.3%
5 184043
8.9%
0 178718
8.7%
6 111557
 
5.4%
7 67651
 
3.3%
8 39141
 
1.9%
9 24720
 
1.2%
Other values (22) 23380
 
1.1%
ValueCountFrequency (%)
0 178718
8.7%
1 436018
21.2%
2 386054
18.8%
3 332961
16.2%
4 272524
13.3%
5 184043
8.9%
6 111557
 
5.4%
7 67651
 
3.3%
8 39141
 
1.9%
9 24720
 
1.2%
ValueCountFrequency (%)
39 1
 
< 0.1%
38 1
 
< 0.1%
31 1
 
< 0.1%
28 4
 
< 0.1%
27 2
 
< 0.1%
26 4
 
< 0.1%
25 9
 
< 0.1%
24 17
< 0.1%
23 16
< 0.1%
22 35
< 0.1%

DefectsB
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28868656
Minimum0
Maximum31
Zeros1818491
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:40.702869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0427831
Coefficient of variation (CV)3.6121635
Kurtosis39.994671
Mean0.28868656
Median Absolute Deviation (MAD)0
Skewness5.3886684
Sum593761
Variance1.0873965
MonotonicityNot monotonic
2023-04-17T16:09:40.771631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 1818491
88.4%
1 100523
 
4.9%
2 51328
 
2.5%
3 33807
 
1.6%
4 21480
 
1.0%
5 12672
 
0.6%
6 7150
 
0.3%
7 4409
 
0.2%
8 2716
 
0.1%
9 1647
 
0.1%
Other values (18) 2544
 
0.1%
ValueCountFrequency (%)
0 1818491
88.4%
1 100523
 
4.9%
2 51328
 
2.5%
3 33807
 
1.6%
4 21480
 
1.0%
5 12672
 
0.6%
6 7150
 
0.3%
7 4409
 
0.2%
8 2716
 
0.1%
9 1647
 
0.1%
ValueCountFrequency (%)
31 2
 
< 0.1%
29 1
 
< 0.1%
27 1
 
< 0.1%
25 2
 
< 0.1%
24 4
 
< 0.1%
22 3
 
< 0.1%
21 5
 
< 0.1%
20 14
< 0.1%
19 16
< 0.1%
18 21
< 0.1%

DefectsC
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021772033
Minimum0
Maximum15
Zeros2019204
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:40.840460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17767257
Coefficient of variation (CV)8.1605869
Kurtosis246.95173
Mean0.021772033
Median Absolute Deviation (MAD)0
Skewness11.981021
Sum44780
Variance0.031567543
MonotonicityNot monotonic
2023-04-17T16:09:40.899742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 2019204
98.2%
1 32494
 
1.6%
2 3607
 
0.2%
3 1021
 
< 0.1%
4 308
 
< 0.1%
5 78
 
< 0.1%
6 33
 
< 0.1%
7 8
 
< 0.1%
8 7
 
< 0.1%
9 3
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 2019204
98.2%
1 32494
 
1.6%
2 3607
 
0.2%
3 1021
 
< 0.1%
4 308
 
< 0.1%
5 78
 
< 0.1%
6 33
 
< 0.1%
7 8
 
< 0.1%
8 7
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 3
 
< 0.1%
8 7
 
< 0.1%
7 8
 
< 0.1%
6 33
 
< 0.1%
5 78
 
< 0.1%
4 308
< 0.1%

Defects0
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10551997
Minimum0
Maximum5
Zeros1854322
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:40.962157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3303999
Coefficient of variation (CV)3.1311598
Kurtosis10.824729
Mean0.10551997
Median Absolute Deviation (MAD)0
Skewness3.2176102
Sum217030
Variance0.1091641
MonotonicityNot monotonic
2023-04-17T16:09:41.023253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1854322
90.2%
1 188429
 
9.2%
2 13487
 
0.7%
3 493
 
< 0.1%
4 32
 
< 0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
0 1854322
90.2%
1 188429
 
9.2%
2 13487
 
0.7%
3 493
 
< 0.1%
4 32
 
< 0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
5 4
 
< 0.1%
4 32
 
< 0.1%
3 493
 
< 0.1%
2 13487
 
0.7%
1 188429
 
9.2%
0 1854322
90.2%

Defects1
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75027215
Minimum0
Maximum14
Zeros1029946
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:41.083447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93561068
Coefficient of variation (CV)1.2470284
Kurtosis3.0375199
Mean0.75027215
Median Absolute Deviation (MAD)0
Skewness1.4616904
Sum1543135
Variance0.87536734
MonotonicityNot monotonic
2023-04-17T16:09:41.142323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 1029946
50.1%
1 650254
31.6%
2 272738
 
13.3%
3 78405
 
3.8%
4 18299
 
0.9%
5 4880
 
0.2%
6 1498
 
0.1%
7 500
 
< 0.1%
8 156
 
< 0.1%
9 64
 
< 0.1%
Other values (4) 27
 
< 0.1%
ValueCountFrequency (%)
0 1029946
50.1%
1 650254
31.6%
2 272738
 
13.3%
3 78405
 
3.8%
4 18299
 
0.9%
5 4880
 
0.2%
6 1498
 
0.1%
7 500
 
< 0.1%
8 156
 
< 0.1%
9 64
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 1
 
< 0.1%
11 6
 
< 0.1%
10 19
 
< 0.1%
9 64
 
< 0.1%
8 156
 
< 0.1%
7 500
 
< 0.1%
6 1498
 
0.1%
5 4880
 
0.2%
4 18299
0.9%

Defects2
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16201543
Minimum0
Maximum9
Zeros1751198
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:41.210102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40614179
Coefficient of variation (CV)2.5068092
Kurtosis7.3700734
Mean0.16201543
Median Absolute Deviation (MAD)0
Skewness2.5851972
Sum333228
Variance0.16495115
MonotonicityNot monotonic
2023-04-17T16:09:41.263329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1751198
85.1%
1 279961
 
13.6%
2 23790
 
1.2%
3 1633
 
0.1%
4 153
 
< 0.1%
5 19
 
< 0.1%
6 12
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 1751198
85.1%
1 279961
 
13.6%
2 23790
 
1.2%
3 1633
 
0.1%
4 153
 
< 0.1%
5 19
 
< 0.1%
6 12
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
6 12
 
< 0.1%
5 19
 
< 0.1%
4 153
 
< 0.1%
3 1633
 
0.1%
2 23790
 
1.2%
1 279961
 
13.6%
0 1751198
85.1%

Defects3
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15722102
Minimum0
Maximum7
Zeros1777189
Zeros (%)86.4%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:41.320458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42654135
Coefficient of variation (CV)2.7130046
Kurtosis11.616587
Mean0.15722102
Median Absolute Deviation (MAD)0
Skewness3.0891507
Sum323367
Variance0.18193752
MonotonicityNot monotonic
2023-04-17T16:09:41.378475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1777189
86.4%
1 241856
 
11.8%
2 32493
 
1.6%
3 4517
 
0.2%
4 603
 
< 0.1%
5 93
 
< 0.1%
6 15
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 1777189
86.4%
1 241856
 
11.8%
2 32493
 
1.6%
3 4517
 
0.2%
4 603
 
< 0.1%
5 93
 
< 0.1%
6 15
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 15
 
< 0.1%
5 93
 
< 0.1%
4 603
 
< 0.1%
3 4517
 
0.2%
2 32493
 
1.6%
1 241856
 
11.8%
0 1777189
86.4%

Defects4
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58304173
Minimum0
Maximum18
Zeros1197647
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:41.446238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84225732
Coefficient of variation (CV)1.4445918
Kurtosis6.6738151
Mean0.58304173
Median Absolute Deviation (MAD)0
Skewness1.9626944
Sum1199181
Variance0.70939739
MonotonicityNot monotonic
2023-04-17T16:09:41.510149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 1197647
58.2%
1 612240
29.8%
2 182829
 
8.9%
3 45116
 
2.2%
4 12678
 
0.6%
5 3917
 
0.2%
6 1450
 
0.1%
7 511
 
< 0.1%
8 221
 
< 0.1%
9 82
 
< 0.1%
Other values (8) 76
 
< 0.1%
ValueCountFrequency (%)
0 1197647
58.2%
1 612240
29.8%
2 182829
 
8.9%
3 45116
 
2.2%
4 12678
 
0.6%
5 3917
 
0.2%
6 1450
 
0.1%
7 511
 
< 0.1%
8 221
 
< 0.1%
9 82
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 1
 
< 0.1%
15 4
 
< 0.1%
14 2
 
< 0.1%
13 6
 
< 0.1%
12 9
 
< 0.1%
11 17
 
< 0.1%
10 36
 
< 0.1%
9 82
 
< 0.1%
8 221
< 0.1%

Defects5
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88070793
Minimum0
Maximum11
Zeros681951
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:41.583409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78644004
Coefficient of variation (CV)0.89296351
Kurtosis1.6030931
Mean0.88070793
Median Absolute Deviation (MAD)1
Skewness0.88676022
Sum1811411
Variance0.61848794
MonotonicityNot monotonic
2023-04-17T16:09:41.643450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1013613
49.3%
0 681951
33.2%
2 298185
 
14.5%
3 53366
 
2.6%
4 7612
 
0.4%
5 1527
 
0.1%
6 392
 
< 0.1%
7 86
 
< 0.1%
8 25
 
< 0.1%
9 8
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 681951
33.2%
1 1013613
49.3%
2 298185
 
14.5%
3 53366
 
2.6%
4 7612
 
0.4%
5 1527
 
0.1%
6 392
 
< 0.1%
7 86
 
< 0.1%
8 25
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 1
 
< 0.1%
9 8
 
< 0.1%
8 25
 
< 0.1%
7 86
 
< 0.1%
6 392
 
< 0.1%
5 1527
 
0.1%
4 7612
 
0.4%
3 53366
 
2.6%
2 298185
14.5%

Defects6
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3042654
Minimum0
Maximum20
Zeros558268
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:41.711595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2007246
Coefficient of variation (CV)0.9206137
Kurtosis3.6380908
Mean1.3042654
Median Absolute Deviation (MAD)1
Skewness1.3404842
Sum2682570
Variance1.4417395
MonotonicityNot monotonic
2023-04-17T16:09:41.781051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 759418
36.9%
0 558268
27.1%
2 453695
22.1%
3 184811
 
9.0%
4 65075
 
3.2%
5 22147
 
1.1%
6 7881
 
0.4%
7 3037
 
0.1%
8 1347
 
0.1%
9 517
 
< 0.1%
Other values (10) 571
 
< 0.1%
ValueCountFrequency (%)
0 558268
27.1%
1 759418
36.9%
2 453695
22.1%
3 184811
 
9.0%
4 65075
 
3.2%
5 22147
 
1.1%
6 7881
 
0.4%
7 3037
 
0.1%
8 1347
 
0.1%
9 517
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 1
 
< 0.1%
17 3
 
< 0.1%
16 5
 
< 0.1%
15 8
 
< 0.1%
14 22
 
< 0.1%
13 35
 
< 0.1%
12 76
 
< 0.1%
11 156
< 0.1%
10 264
< 0.1%

Defects7
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024375634
Minimum0
Maximum6
Zeros2010529
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:42.065120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.16785634
Coefficient of variation (CV)6.8862349
Kurtosis81.888556
Mean0.024375634
Median Absolute Deviation (MAD)0
Skewness7.9868485
Sum50135
Variance0.028175751
MonotonicityNot monotonic
2023-04-17T16:09:42.119360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 2010529
97.8%
1 42831
 
2.1%
2 3021
 
0.1%
3 307
 
< 0.1%
4 57
 
< 0.1%
5 19
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
0 2010529
97.8%
1 42831
 
2.1%
2 3021
 
0.1%
3 307
 
< 0.1%
4 57
 
< 0.1%
5 19
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5 19
 
< 0.1%
4 57
 
< 0.1%
3 307
 
< 0.1%
2 3021
 
0.1%
1 42831
 
2.1%
0 2010529
97.8%

Defects8
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024430575
Minimum0
Maximum7
Zeros2015787
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:42.182302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18483119
Coefficient of variation (CV)7.5655688
Kurtosis99.432112
Mean0.024430575
Median Absolute Deviation (MAD)0
Skewness9.0747534
Sum50248
Variance0.03416257
MonotonicityNot monotonic
2023-04-17T16:09:42.238722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 2015787
98.0%
1 32933
 
1.6%
2 6941
 
0.3%
3 1006
 
< 0.1%
4 87
 
< 0.1%
5 12
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 2015787
98.0%
1 32933
 
1.6%
2 6941
 
0.3%
3 1006
 
< 0.1%
4 87
 
< 0.1%
5 12
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 12
 
< 0.1%
4 87
 
< 0.1%
3 1006
 
< 0.1%
2 6941
 
0.3%
1 32933
 
1.6%
0 2015787
98.0%

Defects9
Real number (ℝ)

SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0024718405
Minimum0
Maximum8
Zeros2053169
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:42.306077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.066769604
Coefficient of variation (CV)27.0121
Kurtosis1652.6553
Mean0.0024718405
Median Absolute Deviation (MAD)0
Skewness35.74091
Sum5084
Variance0.0044581801
MonotonicityNot monotonic
2023-04-17T16:09:42.367351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2053169
99.8%
1 2533
 
0.1%
2 746
 
< 0.1%
3 245
 
< 0.1%
4 54
 
< 0.1%
5 15
 
< 0.1%
6 3
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 2053169
99.8%
1 2533
 
0.1%
2 746
 
< 0.1%
3 245
 
< 0.1%
4 54
 
< 0.1%
5 15
 
< 0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 3
 
< 0.1%
5 15
 
< 0.1%
4 54
 
< 0.1%
3 245
 
< 0.1%
2 746
 
< 0.1%
1 2533
 
0.1%
0 2053169
99.8%

AgeDays
Real number (ℝ)

Distinct12003
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6712.6075
Minimum1214
Maximum45011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 MiB
2023-04-17T16:09:42.454782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1214
5-th percentile3486
Q15437
median6683
Q38041
95-th percentile9602
Maximum45011
Range43797
Interquartile range (IQR)2604

Descriptive statistics

Standard deviation1931.3926
Coefficient of variation (CV)0.28772614
Kurtosis0.77011897
Mean6712.6075
Median Absolute Deviation (MAD)1318
Skewness0.18056745
Sum1.380627 × 1010
Variance3730277.4
MonotonicityNot monotonic
2023-04-17T16:09:42.541554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9237 5834
 
0.3%
8872 5739
 
0.3%
9602 5582
 
0.3%
8507 4868
 
0.2%
9968 4119
 
0.2%
10333 3257
 
0.2%
8141 3126
 
0.2%
10698 2383
 
0.1%
11063 2181
 
0.1%
11429 2176
 
0.1%
Other values (11993) 2017502
98.1%
ValueCountFrequency (%)
1214 4
< 0.1%
1217 3
< 0.1%
1221 2
< 0.1%
1222 1
 
< 0.1%
1224 3
< 0.1%
1225 1
 
< 0.1%
1229 1
 
< 0.1%
1231 1
 
< 0.1%
1232 4
< 0.1%
1235 1
 
< 0.1%
ValueCountFrequency (%)
45011 1
 
< 0.1%
44863 1
 
< 0.1%
44670 1
 
< 0.1%
41433 1
 
< 0.1%
31883 1
 
< 0.1%
25308 3
< 0.1%
25071 1
 
< 0.1%
24943 1
 
< 0.1%
24185 1
 
< 0.1%
24138 2
< 0.1%

Interactions

2023-04-17T16:09:24.491655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:38.455810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:41.533225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:44.461335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:47.597126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:50.525821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:53.458479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:56.392488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:59.507547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:02.411700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:05.436304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:08.555989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:11.971736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:15.237872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:18.206608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:21.170858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:24.711445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:38.670762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:41.707895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:44.654249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:47.777537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:50.705635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:53.638529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:56.587139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:59.685385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:02.597998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:05.631212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:08.743358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:12.183910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:15.422099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:18.391293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:21.363746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:24.905034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:38.862988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:41.886752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:44.843716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:47.959382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:50.887933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:53.820060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:56.781790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:59.864650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:02.787062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:05.822253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:08.935547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:12.382211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:15.625149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:18.576668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:21.552114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:25.116777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:39.057811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:42.073519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:45.043824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:48.142702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:51.076746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:54.009040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:56.980222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:00.052430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:02.985150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:06.024593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:09.128310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:12.585265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:15.814113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:18.768520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:21.739926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:25.308258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:39.247167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:42.255123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:45.237000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:48.324403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:51.254099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:54.192333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:57.172923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:00.232432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:03.184066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:06.220343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:09.312825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:12.781374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:15.996345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:18.952548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:21.923525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:25.519421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:39.440898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:42.442469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:45.436153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:48.511499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:51.439380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:54.371260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:57.365550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:00.419508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:03.394824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:06.419781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:09.885221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:12.979987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:16.183642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:19.142959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:22.106312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:25.705276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:39.627858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:42.623997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:45.626925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:48.691385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:51.619891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:54.554413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:57.555099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:00.597260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:03.572991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:06.611779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:10.067149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:13.172712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:16.360496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:19.320279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:22.285254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:25.893491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:39.820362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:42.807411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:45.824462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:48.876817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:51.803675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:54.738348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:57.749030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:00.775764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:03.759838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:06.808845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:10.256533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:13.385912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:16.549815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:19.504508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:22.471509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:26.084196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:40.006203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:42.985743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:46.016086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:49.055925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:51.983640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:54.916989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:57.933934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:00.947933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:03.940035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:06.995920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:10.437019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:13.602475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:16.731507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:19.680725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:22.649611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:26.315964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:40.201079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:43.169997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:46.215369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:49.239929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:52.168240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:55.099954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:58.129721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:01.124982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:04.127634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:07.188200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:10.628025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:13.817680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:16.920350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:19.864040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:22.834012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:26.512719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:40.398721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:43.353162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:46.422045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:49.426667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:52.353209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:55.284373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:58.335122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:01.311531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:04.325275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:07.380323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:10.818565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:14.050397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:17.105877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:20.066688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:23.024340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:26.700157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:40.589293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:43.534291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:46.614831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:49.607873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:52.533098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:55.463095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:58.526776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:01.491333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:04.506284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:07.578829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:11.007231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:14.243493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:17.282914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:20.249474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:23.212349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:26.896533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:40.781808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:43.722766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:46.814806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:49.796480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:52.723396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:55.653989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:58.724458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:01.684831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:04.695632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:07.775978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:11.198165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:14.453620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:17.466307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:20.442243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:23.419157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:27.087728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:40.970956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:43.904138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:47.009741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:49.977949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:52.906994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:55.834429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:58.917432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:01.860537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:04.877919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:07.969064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:11.380957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:14.653704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:17.650878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:20.622491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:23.615603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:27.293094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:41.163146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:44.085631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:47.207464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:50.164303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:53.090755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:56.019477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:59.128578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:02.047544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:05.063277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:08.163920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:11.588110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:14.852355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:17.837325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:20.804648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:23.798083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:27.505895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:41.358785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:44.272993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:47.410091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:50.349709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:53.278507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:56.207513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:08:59.330581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:02.231499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:05.249294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:08.363232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:11.783952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:15.060671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:18.029790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:20.994094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:09:24.277843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-17T16:09:28.626182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-17T16:09:31.406226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-17T16:09:36.688376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays
StationId
3512pravidelnáTAH31100EF11120502019-01-16 11:36:42.41760D/3AEBINÁKLADNÍ AUTOMOBILMT 740N2G2016-01-0718719způsobilé330001000210002657
3762pravidelnáTN9FV1Z00RAAM50322019-01-09 09:42:53.517781.135NAGMNÁKLADNÍ AUTOMOBILVARIANTN11994-01-0185753způsobilé3300010010100010698
3427evidenčníRLVMEM5057VN000702019-01-31 15:21:19.753FBGMAIEMOTOCYKLMAGMAX 275 HTLE2006-11-2815380částečně způsobilé401010000000005984
3310pravidelnáZAR162B40000752612019-01-14 07:37:22.723ARO6166ALFA ROMEOOSOBNÍ AUTOMOBIL75M11989-01-01239430způsobilé1600020011300012524
3530opakovanáZAR930000022617742019-01-29 10:58:24.55767601ALFA ROMEOOSOBNÍ AUTOMOBIL145M12000-05-25218070způsobilé230002000110008362
3712pravidelnáZAR930000022607182019-01-31 05:56:00.390AR32302ALFA ROMEOOSOBNÍ AUTOMOBIL145M12000-03-31194848způsobilé440002010110008417
3528pravidelnáZAR930000022695032019-01-10 11:23:41.817AR 33503ALFA ROMEOOSOBNÍ AUTOMOBIL145M12000-12-28165477způsobilé410000000110008145
3425pravidelnáZAR930000040130592019-01-15 09:34:57.943AR 33201ALFA ROMEOOSOBNÍ AUTOMOBIL145M11996-06-24195900způsobilé260004000020009793
3205pravidelnáZAR930000022667102019-01-16 13:00:46.883AR33503ALFA ROMEOOSOBNÍ AUTOMOBIL145M12000-08-31228177způsobilé3110023101320008264
3310pravidelnáZAR930000041019502019-01-18 08:51:46.033AR 33503ALFA ROMEOOSOBNÍ AUTOMOBIL145M11997-04-14152412způsobilé550011001120009499
TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays
StationId
3754pravidelnáTMBFE61Z3C20429092019-12-31CFHCŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12011-09-01201986způsobilé220010010100004246
3603pravidelnáWF0NXXGCDNYA566782019-12-31FYDDFORDOSOBNÍ AUTOMOBILFOCUSM12000-11-30188112způsobilé220000002110008173
3316pravidelnáTMBHS61Z5621496302019-12-31BKCŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12005-09-22189426způsobilé230000000310006416
3307na žádost zákazníkaTMBHP21U1326994202019-12-31ASVŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12002-09-020částečně způsobilé201000001000007532
3234pravidelnáWF0AXXGAJAYS650472019-12-31DHFORDOSOBNÍ AUTOMOBILFIESTAM12001-03-15152808způsobilé240001000130008068
3609pravidelnáTMBKS21U3386785372019-12-31ATDŠKODAOSOBNÍ AUTOMOBILOCTAVIA COMBIM12002-10-03174858způsobilé250002002120007501
3212opakovanáTMBJF46Y4439272072019-12-31ASYŠKODAOSOBNÍ AUTOMOBILFABIA COMBIM12003-09-29306512způsobilé260011000230007140
3108pravidelnáWVWZZZ1KZ6B0795032019-12-31BCAVOLKSWAGENOSOBNÍ AUTOMOBILGOLFM12005-12-15148461způsobilé230010100110006332
3609pravidelnáTMBEFF613153526902019-12-31781.136MŠKODAOSOBNÍ AUTOMOBILFELICIAM12001-10-19178343způsobilé240001000220007850
3506opakovanáU5YHB811AAL1334922019-12-31G4FAKIAOSOBNÍ AUTOMOBILCEEDM12010-04-2178328nezpůsobilé205000002100204744

Duplicate rows

Most frequently occurring

TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays# duplicates
0evidenční3D4GGH7Y09T5488802019-05-15BWDDODGEOSOBNÍ AUTOMOBILJOURNEYM12010-01-20134190částečně způsobilé3010100000000048352
1evidenčníKL1NF35315K1495682019-05-14T18SEDCHEVROLETOSOBNÍ AUTOMOBILNUBIRAM12005-04-05203736částečně způsobilé2010100000000065862
2evidenčníKMFYKN7HP6U0807172019-07-19D4BHHYUNDAINÁKLADNÍ AUTOMOBILH1N12006-09-27215505částečně způsobilé5010100000000060462
3evidenčníTMBWU26Y0439702952019-07-19ASZŠKODAOSOBNÍ AUTOMOBILFABIAM12003-11-18256907částečně způsobilé5010100000000070902
4evidenčníTMBZZZ1U1V20175172019-06-27AEEŠKODAOSOBNÍ AUTOMOBILOCTAVIAM11997-05-29287077způsobilé4100100000000094542
5evidenčníVF1BZAS05446208512019-09-03K9K H8RENAULTOSOBNÍ AUTOMOBILMEGANEM12011-02-01317250částečně způsobilé2010100000000044582
6evidenčníVF1MA0005568009392019-04-04M9T C7RENAULTNÁKLADNÍ AUTOMOBILMASTERN12016-11-1537965částečně způsobilé4010100000000023442
7evidenčníWBAAP91000JH859392019-08-05306D1BMWOSOBNÍ AUTOMOBIL330M12000-04-26254423částečně způsobilé1010100000000083912
8evidenčníWDB9702571L1117842019-07-30OM 906 LA III/2MERCEDES-BENZNÁKLADNÍ AUTOMOBILATEGON22006-04-10721302částečně způsobilé2020200000000062162
9evidenčníWVGZZZ1TZAW0547012019-06-27BSEVOLKSWAGENOSOBNÍ AUTOMOBILTOURANM12010-02-2593805částečně způsobilé4010100000000047992